Data Analyst - 12 month FTC

Legend
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Overview

We are Legend. We exist to build legendary experiences, by growing winning brands that people love, together. For over 20 years, we’ve built cutting-edge products in the world’s most competitive comparison markets: iGaming, Sports Betting, and Personal Finance. We’re 700+ Legends strong. As a team, we’re growing an industry-leading portfolio that continues to attract and entertain hundreds of millions of people worldwide. You might not know our name, but we power some of the biggest iGaming, Sports Betting and Personal Finance brands worldwide. We drive performance behind the scenes, and let our results do the talking. Our company has a unique DNA and culture nurtured over two decades. Everything we do is driven by our five core values: Rocking It, Win as One, Build the Future, Shipping Greatness, and Play to Win. These form an energy and team spirit that inspires us to do our best work. From amplified career paths to supercharged online journeys for our users, we deliver magic rooted in method. Want in? Unlock the Legend in you.


Your path to legendary impact starts here: Does the idea of tackling complex data problems get you fired up on a Monday morning? Are you motivated by sharpening your craft, owning your impact, and collaborating with brilliant minds?


The foundation is live, and now it’s your turn to take our commercial data reporting to the next level. You’ll transform complex commercial data into clear, actionable insights, that will underpin smarter partner decisions, improved campaign performance, and more accurate revenue forecasting. Your insights will directly impact our ability to maximise revenue through better top listing decisions, insight opportunity detection and partner relationship management. Help our users. Grow your career. Win.


What you’ll positively impact

  • Drive revenue growth through clear commercial insights.
  • Improve partner and campaign performance visibility.
  • Mitigate revenue leakage by surfacing data quality concerns.
  • Streamline reporting through automation, creating space for strategic priorities.
  • Deliver lasting value through clear documentation.
  • Strengthen collaboration between data and commercial teams through reliable analytics.
  • Contribute to a proactive, data-driven culture focused on measurable outcomes.

What makes you a potential Legend

  • We seek resourceful and kind self-starters who enjoy autonomy, a dynamic environment, and high accountability. You’ll thrive here if you’re always eager to learn, value constructive feedback, and are excited by big goals.

In terms of technical skills for the Data Analyst role, you ideally bring:



  • Expert SQL skills to shape varied data sources into clear, actionable datasets for dashboards and broader analytics.
  • Strong experience with Tableau or Power BI.

Business & Analytical Skills

  • Ability to translate complex data into clear insights and reporting.
  • Proficiency in data validation.
  • Expertise in automation of reporting and data workflows.
  • Commercial acumen and analytical problem-solving.
  • Effective stakeholder communication of analytical insights and recommendations.

Soft Skills

  • Excellent stakeholder communication.
  • For this role as Data Analyst, you will have multiple chances to progress further with us over time.
  • We can’t make any iron-clad promises as to timelines, as more senior roles are unlocked by us reaching our organisational targets. But an example could include growing into a Content Manager role taking ownership of multiple markets through a team of editors.
  • Once you’ve finished onboarding, we will make an IDP together, helping you reach your long-term career ambitions.

Our culture & benefits

  • We thrive from helping our team members grow their skills and careers. It\'s one of our biggest motivations as a company.
  • We regularly check in with our team and have strong employee engagement scores. Being #1 is only worth it if we’re having fun along the journey.
  • We offer flexible working hours. Our core hours are from 10 am to 3 pm your local time.
  • We benchmark our compensation packages annually which tells us what the market is paying. When we say we offer a “competitive package,” we mean it. We also have a long-term incentive plan so we can all share in the growth and success of Legend.
  • We offer generous annual leave, including our company shutdown between Christmas and New Year’s, birthdays, volunteer and life event days.
  • We host exciting global Legend events, where we unite in person to ignite our shared passion and unveil the exciting strategies for the year ahead!

Are you ready? The steps to unlock the Legend in YOU.



  • Then your Legendary journey begins - find out more about the role and process here


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.